论文标题
ALRT：一个不规则采样时间数据的主动学习框架
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data
论文作者
论文摘要
败血症是一种致命的疾病，影响了医院的许多患者。最近的研究表明，由于人体对感染的功能失调的宿主反应，被诊断出患有败血症的患者具有显着的死亡率和发病率。临床医生通常依靠使用顺序器官衰竭评估（SOFA），全身性炎症反应综合征（SIRS）和修改后的预警评分（MEWS）来确定需要进一步检查和治疗的临床恶化的早期签名。但是，这些工具中的许多是手动计算的，并且不是为自动计算而设计的。有不同的方法用于开发败血症的发作模型，但是其中许多模型必须接受足够数量的患者观察培训，以形成准确的败血症预测。此外，败血症患者的准确注释是一项重大持续的挑战。在本文中，我们建议使用主动学习复发性神经网络（ALRT）用于短暂的时间范围，以改善对不规则采样的时间事件（如败血症）的预测。我们表明，在有限数据上训练的主动学习RNN模型可以形成与整个培训数据集相当的稳健败血症预测。
Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computation. There have been different methods used for developing sepsis onset models, but many of these models must be trained on a sufficient number of patient observations in order to form accurate sepsis predictions. Additionally, the accurate annotation of patients with sepsis is a major ongoing challenge. In this paper, we propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.
